Efficient Restaurant Management System Through Machine Learning and NLP-Based Sentiment Analysis

The rapid growth of online restaurant reviews has made sentiment analysis a crucial tool for understanding customer feedback and enhancing service quality. Restaurant quality including food and drinks, environment, place, and service directly impacts brand image and determines customer satisfaction. Traditional review evaluation methods are time-consuming and inefficient, creating a need for automated sentiment classification. This study shows an optimized restaurant management system that introduces machine learning (ML) and natural language processing (NLP) techniques to study customer sentiments. By evaluating these sentiments, businesses can obtain valuable observations into customer satisfaction and recognize areas of improvement. The methodology consists of data preprocessing, feature extraction, and selection of the best ML model from a set containing Decision Trees, Support Vector Machines, Random Forest, and XGBoost, to rank reviews into positive, negative, or neutral sentiments. Additionally, voice search and bulk testing functionalities enhance system usability. Experimental results display high classification accuracy, enabling restaurant owners to glean insightful information from customer reviews. By streamlining sentiment analysis, this system aids in data-driven decision-making, ultimately improving customer satisfaction and operational efficiency.